
The engineering aspect of collecting, storing, and organizing the one million SRFs with multiple resolutions is as challenging as training properly on SRFs. In order to do that in manageable time and memory, while maintaining high quality in the optimized samples, a set of strategies is employed. The dimension of the radiance color \(d\)  is chosen to be \(d=3\times 4=12\)  of 4 spherical harmonics factors of RGB channels for view-dependent SRF and \(d=3\times 1=3\)  for fixed RGB colors of the SRF. Since the input partial SRFs are noisy, we use \(d=3\)  while the final output SRFs use \(d=12\)  for high-quality image generation.
Using fewer Spherical Harmonics components (from 9 to 4 per RGB channel) reduces the optimization space by 40% and time by 10%, while maintaining the same PSNR. Using RGB as colors instead of SH factors reduces PSNR by \(\sim 1\)  dB, space by 80%, and time by 20%. Running Plenoxels [1]} for fewer iterations (3\(\times \) 12K) reduces the time by 30% while maintaining the same PSNR. Using 400 views/shapes in SPARF to optimize the SRFs keep the time manageable in optimization (\(\sim 4\)  minutes for the 512 resolution) while maintaining high PSNR (\(\sim 30\) dB). When saving the SRFs, we only save the set of coordinates (integers) and float features (densities and radiance components). A total of four variants of the partial SRFs are collected for all the resolutions and the partials use 1 and 3 images. The anatomy of the distribution of classes and SRFs in SPARF is presented in Figure REF . More details about SPARF and visualizations of some of its samples are available in the supplementary material.
